evaluate_Weka_classifier

0th

Percentile

Model Statistics for R/Weka Classifiers

Compute model performance statistics for a fitted Weka classifier.

Keywords
models
Usage
evaluate_Weka_classifier(object, newdata = NULL, cost = NULL,
numFolds = 0, complexity = FALSE,
class = FALSE, seed = NULL, ...)
Arguments
object
a Weka_classifier object.
newdata
an optional data frame in which to look for variables with which to evaluate. If omitted or NULL, the training instances are used.
cost
a square matrix of (mis)classification costs.
numFolds
the number of folds to use in cross-validation.
complexity
option to include entropy-based statistics.
class
option to include class statistics.
seed
optional seed for cross-validation.
...
further arguments passed to other methods (see details).
Details

The function computes and extracts a non-redundant set of performance statistics that is suitable for model interpretation. By default the statistics are computed on the training data.

Currently argument ... only supports the logical variable normalize which tells Weka to normalize the cost matrix so that the cost of a correct classification is zero.

Note that if the class variable is numeric only a subset of the statistics are available. Arguments complexity and class are then not applicable and therefore ignored.

Value

• An object of class Weka_classifier_evaluation, a list of the following components:
• stringcharacter, concatenation of the string representations of the performance statistics.
• detailsvector, base statistics, e.g., the percentage of instances correctly classified, etc.
• detailsComplexityvector, entropy-based statistics (if selected).
• detailsClassmatrix, class statistics, e.g., the true positive rate, etc., for each level of the response variable (if selected).
• confusionMatrixtable, cross-classification of true and predicted classes.

References

I. H. Witten and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.

Aliases
• evaluate_Weka_classifier
Examples
## Use some example data.
package = "RWeka"))

## Identify a decision tree.
m <- J48(play~., data = w)
m

## Use 10 fold cross-validation.
e <- evaluate_Weka_classifier(m,
cost = matrix(c(0,2,1,0), ncol = 2),
numFolds = 10, complexity = TRUE,
seed = 123, class = TRUE)
e
summary(e)
e\$details
Documentation reproduced from package RWeka, version 0.4-18, License: GPL-2

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